Queueing Network-Model Human Processor (QN-MHP)

Liu, Yili; Feyen, Robert; Tsimhoni, Omer

doi:10.1145/1143518.1143520

Cited by 163 publications

(23 citation statements)

References 82 publications

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“…They could, however, learn distinct stimulus and response sequences simultaneously when one of them was coded as a color-sequence; this allowed the learning of the location sequence to take place without interference. Similarly, the Queueing Network-Model Human Processor, which models human behavior during concurrent driving tasks and multitasking performance more broadly, assumes that tasks can share resources in the central processing domain and that interference occurs most often when tasks compete for peripheral (e.g., visual) resources (Liu et al, 2006). Taken together, these findings begin to address the nature of the codes described in Wickens’ (1984) theory and what causes crosstalk.…”

Section: Discussionmentioning

confidence: 99%

Separation of Tasks Into Distinct Domains, Not Set-Level Compatibility, Minimizes Dual-Task Interference

Halvorson

Hazeltine

2019

Front. Psychol.

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Dual-task costs are often significantly reduced or eliminated when both tasks use compatible stimulus-response (S-R) pairs. Either by design or unintentionally, S-R pairs used in dual-task experiments that produce small dual-task costs typically have two properties that may reduce dual-task interference. One property is that they are easy to keep separate; specifically, one task is often visual-spatial and contains little verbal information and the other task is primarily auditory-verbal and has no significant spatial component. The other property is that the two sets of S-R pairs are often compatible at the set-level; specifically, the collection of stimuli for each task is strongly related to the collection of responses for that task, even if there is no direct correspondence between the individual items in the sets. In this paper, we directly test which of these two properties is driving the absence of large dual-task costs. We used stimuli (images of hands and auditory words) that when previously been paired with responses (button presses and vocal utterances) produced minimal dual-task costs, but we manipulated the shape of the hands in the images and the auditory words. If set-level compatibility is driving efficient performance, then these changes should not affect dual-task costs. However, we found large changes in the dual-task costs depending on the specific stimuli and responses. We conclude that set-level compatibility is not sufficient to minimize dual-task costs. We connect these findings to divisions within the working memory system and discuss implications for understanding dual-task performance more broadly.

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Section: Discussionmentioning

confidence: 99%

Separation of Tasks Into Distinct Domains, Not Set-Level Compatibility, Minimizes Dual-Task Interference

Halvorson

Hazeltine

2019

Front. Psychol.

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“…A task at a certain level is referred to as an exercise in this paper. Preliminary task analysis based on the human processor model (Card, Moran, & Newell, 1986; Y. Liu, Feyen, & Tsimhoni, 2006) and Therbligs (Gilbreth & Kent, 1911) was conducted to briefly describe the task demands across task levels.…”

Section: Methodsmentioning

confidence: 99%

Eye-Tracking Metrics Predict Perceived Workload in Robotic Surgical Skills Training

Wu¹,

Cha

Sulek

et al. 2019

Hum Factors

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Objective: The aim of this study is to assess the relationship between eye-tracking measures and perceived workload in robotic surgical tasks. Background: Robotic techniques provide improved dexterity, stereoscopic vision, and ergonomic control system over laparoscopic surgery, but the complexity of the interfaces and operations may pose new challenges to surgeons and compromise patient safety. Limited studies have objectively quantified workload and its impact on performance in robotic surgery. Although not yet implemented in robotic surgery, minimally intrusive and continuous eye-tracking metrics have been shown to be sensitive to changes in workload in other domains. Methods: Eight surgical trainees participated in 15 robotic skills simulation sessions. In each session, participants performed up to 12 simulated exercises. Correlation and mixed-effects analyses were conducted to explore the relationships between eye-tracking metrics and perceived workload. Machine learning classifiers were used to determine the sensitivity of differentiating between low and high workload with eye-tracking features. Results: Gaze entropy increased as perceived workload increased, with a correlation of .51. Pupil diameter and gaze entropy distinguished differences in workload between task difficulty levels, and both metrics increased as task level difficulty increased. The classification model using eye-tracking features achieved an accuracy of 84.7% in predicting workload levels. Conclusion: Eye-tracking measures can detect perceived workload during robotic tasks. They can potentially be used to identify task contributors to high workload and provide measures for robotic surgery training. Application: Workload assessment can be used for real-time monitoring of workload in robotic surgical training and provide assessments for performance and learning.

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“…In this scenario a human subject undertakes a simultaneous CCTV monitoring task while multiple sensors track the environment. Therefore, our system can be widely utilized in various multi-human-multiagents system task scenarios, such as security monitoring [49], [64], air traffic management [50], [65], [66], and performance checking [51], [67]. Additionally, we only validated the AWAC on homogeneous missions and robot platforms, which limits its generalizability.…”

Section: Discussionmentioning

confidence: 99%

Affective Workload Allocation for Multi-human Multi-robot Teams

Jo¹,

Wang²,

Yang³

et al. 2023

Preprint

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The interaction and collaboration between humans and multiple robots represent a novel field of research known as human multi-robot systems. Adequately designed systems within this field allow teams composed of both humans and robots to work together effectively on tasks such as monitoring, exploration, and search and rescue operations. This paper presents a deep reinforcement learning-based affective workload allocation controller specifically for multi-human multi-robot teams. The proposed controller can dynamically reallocate workloads based on the performance of the operators during collaborative missions with multi-robot systems. The operators' performances are evaluated through the scores of a self-reported questionnaire (i.e., subjective measurement) and the results of a deep learning-based cognitive workload prediction algorithm that uses physiological and behavioral data (i.e., objective measurement). To evaluate the effectiveness of the proposed controller, we use a multi-human multi-robot CCTV monitoring task as an example and carry out comprehensive real-world experiments with 32 human subjects for both quantitative measurement and qualitative analysis. Our results demonstrate the performance and effectiveness of the proposed controller and highlight the importance of incorporating both subjective and objective measurements of the operators' cognitive workload as well as seeking consent for workload transitions, to enhance the performance of multi-human multi-robot teams.

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Queueing Network-Model Human Processor (QN-MHP)

Cited by 163 publications

References 82 publications

Separation of Tasks Into Distinct Domains, Not Set-Level Compatibility, Minimizes Dual-Task Interference

Separation of Tasks Into Distinct Domains, Not Set-Level Compatibility, Minimizes Dual-Task Interference

Eye-Tracking Metrics Predict Perceived Workload in Robotic Surgical Skills Training

Affective Workload Allocation for Multi-human Multi-robot Teams

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